US9891628B2 - Sensor-based association of traffic control devices to traffic lanes for autonomous vehicle navigation - Google Patents

Sensor-based association of traffic control devices to traffic lanes for autonomous vehicle navigation Download PDF

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US9891628B2
US9891628B2 US15/173,914 US201615173914A US9891628B2 US 9891628 B2 US9891628 B2 US 9891628B2 US 201615173914 A US201615173914 A US 201615173914A US 9891628 B2 US9891628 B2 US 9891628B2
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vehicle
traffic
control device
data storage
lane
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US20170351266A1 (en
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James N. Nickolaou
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GM Global Technology Operations LLC
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GM Global Technology Operations LLC
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Priority to CN201710341871.3A priority patent/CN107463170A/zh
Priority to DE102017112306.7A priority patent/DE102017112306A1/de
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
    • G05D1/0246—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using a video camera in combination with image processing means
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0268—Control of position or course in two dimensions specially adapted to land vehicles using internal positioning means
    • G05D1/0274—Control of position or course in two dimensions specially adapted to land vehicles using internal positioning means using mapping information stored in a memory device
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
    • B60W30/10—Path keeping
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/005—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 with correlation of navigation data from several sources, e.g. map or contour matching
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/38—Electronic maps specially adapted for navigation; Updating thereof
    • G01C21/3804—Creation or updating of map data
    • G01C21/3833—Creation or updating of map data characterised by the source of data
    • G01C21/3848—Data obtained from both position sensors and additional sensors
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/0088—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0255—Control of position or course in two dimensions specially adapted to land vehicles using acoustic signals, e.g. ultra-sonic singals
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0257—Control of position or course in two dimensions specially adapted to land vehicles using a radar
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0276—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0276—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle
    • G05D1/0278—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle using satellite positioning signals, e.g. GPS

Definitions

  • the present disclosure relates to autonomous vehicles controlled by automated driving systems, particularly those configured to automatically control vehicle steering, acceleration, and braking during a drive cycle without human intervention.
  • Vehicle automation has been categorized into numerical levels ranging from Zero, corresponding to no automation with full human control, to Five, corresponding to full automation with no human control.
  • Various automated driver-assistance systems such as cruise control, adaptive cruise control, and parking assistance systems correspond to lower automation levels, while true “driverless” vehicles correspond to higher automation levels.
  • a system for generating mapping data includes a host vehicle and at least one sensor coupled to the host vehicle.
  • the sensor is capable of detecting a traffic control device and capable of detecting motion of a vehicle.
  • the system additionally includes non-transient data storage and a processor.
  • the processor is in communication with the sensor and the data storage.
  • the processor is configured to, in response to the at least one sensor detecting a traffic control device and detecting motion of a vehicle during a drive cycle, identify a lane of traffic associated with the vehicle, associate the lane of traffic with the traffic control device, and store the association of the lane of traffic with the traffic control device in the data storage for subsequent access by an automated driving system.
  • detecting motion of a vehicle includes detecting motion of the host vehicle.
  • the senor is capable of detecting motion of a target vehicle proximate the host vehicle, and detecting motion of a vehicle includes detecting motion of the target vehicle.
  • the host vehicle is an autonomous vehicle comprising an automated driving system.
  • the automated driving system is configured to, during a subsequent drive cycle, access the data storage.
  • the processor and data storage are coupled to the host vehicle.
  • the non-transient data storage includes a remote data storage.
  • the system may additionally include a wireless communications system coupled to the vehicle, with the processor being in communication with the sensor or the remote data storage via the wireless communications system.
  • a method of controlling a vehicle includes providing a host vehicle with at least one sensor capable of detecting a traffic control device and capable of detecting motion of a vehicle.
  • the method additionally includes, in response to detecting a traffic control device and detecting motion of a vehicle during a drive cycle, identifying a lane of traffic associated with the vehicle and associating the lane of traffic with the traffic control device.
  • the method further includes storing the association of the lane of traffic with the traffic control device in non-transient data storage.
  • detecting motion of a vehicle includes detecting motion of the host vehicle.
  • the senor is capable of detecting motion of a target vehicle proximate the host vehicle, and detecting motion of a vehicle includes detecting motion of the target vehicle.
  • the method additionally includes accessing, by an automated driving system of an autonomous vehicle, the non-transient data storage.
  • the autonomous vehicle may be the host vehicle.
  • the non-transient data storage is remote from the vehicle.
  • the associating is performed by at least one processor associated with the host vehicle or by a server remote from the host vehicle.
  • An autonomous vehicle includes an automated driving system configured to control vehicle steering, acceleration, and braking during a drive cycle.
  • the vehicle additionally includes at least one sensor capable of detecting a traffic control device and capable of detecting motion of a vehicle.
  • the vehicle further includes non-transient data storage and at least one processor.
  • the processor is in communication with the sensor and the data storage.
  • the processor is configured to, in response to the at least one sensor detecting a traffic control device and detecting motion of a vehicle during a drive cycle, identify a lane of traffic associated with the vehicle, associate the lane of traffic with the traffic control device, and store the association of the lane of traffic with the traffic control device in the data storage for subsequent access by the automated driving system.
  • the senor is capable of detecting motion of a target vehicle, distinct from the autonomous vehicle.
  • the vehicle additionally includes a wireless communication device.
  • the processor is further configured to communicate the association of the lane of traffic with the traffic control device to a remote server for subsequent access by an additional automated driving system.
  • the processor may be further configured to receive additional associations of additional lanes of traffic with additional traffic control devices from the remote server.
  • Embodiments according to the present disclosure provide a number of advantages. For example, systems and methods according to the present disclosure may enable faster and less expensive mapping of intersections for navigability by autonomous vehicles.
  • FIG. 1 is a schematic representation of a vehicle according to the present disclosure
  • FIG. 2 is a schematic representation of a system for controlling a vehicle according to the present disclosure
  • FIG. 3 is a flowchart representation of a first embodiment of a method for controlling a vehicle according to the present disclosure
  • FIG. 4 is a flowchart representation of a second embodiment of a method for controlling a vehicle according to the present disclosure
  • FIG. 5 is a flowchart representation of a third embodiment of a method for controlling a vehicle according to the present disclosure
  • FIG. 6 is a flowchart representation of a fourth embodiment of a method for controlling a vehicle according to the present disclosure.
  • FIG. 7 is a flowchart representation of a fifth embodiment of a method for controlling a vehicle according to the present disclosure.
  • the automotive vehicle 10 includes a propulsion system 12 , which may in various embodiments include an internal combustion engine, an electric machine such as a traction motor, and/or a fuel cell propulsion system.
  • a propulsion system 12 may in various embodiments include an internal combustion engine, an electric machine such as a traction motor, and/or a fuel cell propulsion system.
  • the automotive vehicle 10 also includes a transmission 14 configured to transmit power from the propulsion system 12 to vehicle wheels 16 according to selectable speed ratios.
  • the transmission 14 may include a step-ratio automatic transmission, a continuously-variable transmission, or other appropriate transmission.
  • the automotive vehicle 10 additionally includes a steering system 18 . While depicted as including a steering wheel for illustrative purposes, in some embodiments contemplated within the scope of the present disclosure, the steering system 18 may not include a steering wheel.
  • the automotive vehicle 10 additionally includes a plurality of vehicle wheels 16 and associated wheel brakes 20 configured to provide braking torque to the vehicle wheels 16 .
  • the wheel brakes 20 may, in various embodiments, include friction brakes, a regenerative braking system such as an electric machine, and/or other appropriate braking systems.
  • the propulsion system 12 , transmission 14 , steering system 18 , and wheel brakes 20 are in communication with or under the control of at least one controller 22 . While depicted as a single unit for illustrative purposes, the controller 22 may additionally include one or more other controllers, collectively referred to as a “controller.”
  • the controller 22 may include a microprocessor or central processing unit (CPU) in communication with various types of computer readable storage devices or media.
  • Computer readable storage devices or media may include volatile and nonvolatile storage in read-only memory (ROM), random-access memory (RAM), and keep-alive memory (KAM), for example.
  • KAM is a persistent or non-volatile memory that may be used to store various operating variables while the CPU is powered down.
  • Computer-readable storage devices or media may be implemented using any of a number of known memory devices such as PROMs (programmable read-only memory), EPROMs (electrically PROM), EEPROMs (electrically erasable PROM), flash memory, or any other electric, magnetic, optical, or combination memory devices capable of storing data, some of which represent executable instructions, used by the controller 22 in controlling the vehicle.
  • PROMs programmable read-only memory
  • EPROMs electrically PROM
  • EEPROMs electrically erasable PROM
  • flash memory or any other electric, magnetic, optical, or combination memory devices capable of storing data, some of which represent executable instructions, used by the controller 22 in controlling the vehicle.
  • the controller 22 is provided with an automated driving system (ADS) 24 for automatically controlling various actuators in the vehicle 10 .
  • ADS automated driving system
  • the ADS 24 is configured to control the propulsion system 12 , transmission 14 , steering system 18 , and wheel brakes 20 to control vehicle acceleration, steering, and braking, respectively, without human intervention.
  • the ADS 24 is configured to control the propulsion system 12 , transmission 14 , steering system 18 , and wheel brakes 20 in response to inputs from a plurality of sensors 26 , which may include GPS, RADAR, LIDAR, optical cameras, thermal cameras, ultrasonic sensors, accelerometers, and/or additional sensors as appropriate.
  • sensors 26 may include GPS, RADAR, LIDAR, optical cameras, thermal cameras, ultrasonic sensors, accelerometers, and/or additional sensors as appropriate.
  • the vehicle 10 additionally includes a wireless communications system 28 configured to wirelessly communicate with other vehicles (“V2V”) and/or infrastructure (“V2I”).
  • the wireless communication system 28 is configured to communicate via a dedicated short-range communications (DSRC) channel.
  • DSRC channels refer to one-way or two-way short-range to medium-range wireless communication channels specifically designed for automotive use and a corresponding set of protocols and standards.
  • additional or alternate wireless communications standards such as IEEE 802.11 and cellular data communication, are also considered within the scope of the present disclosure.
  • the ADS 24 is a so-called Level Four or Level Five automation system.
  • a Level Four system indicates “high automation”, referring to the driving mode-specific performance by an automated driving system of all aspects of the dynamic driving task, even if a human driver does not respond appropriately to a request to intervene.
  • a Level Five system indicates “full automation”, referring to the full-time performance by an automated driving system of all aspects of the dynamic driving task under all roadway and environmental conditions that can be managed by a human driver.
  • the system 30 includes a wireless communication device 28 ′.
  • the wireless communication device 28 ′ is associated with an autonomous vehicle arranged generally similar to the vehicle 10 as shown in FIG. 1 and discussed above.
  • the wireless communication device 28 ′ is in communication with at least one remote server 32 .
  • the wireless communication device 28 ′ is configured to wirelessly communicate with the server 32 , e.g. via cellular data communication or other appropriate wireless communication protocols.
  • the wireless communication device 28 ′′ is configured to communicate information to the server 32 .
  • the server 32 includes at least one computer readable storage device 34 .
  • the server 32 may include a microprocessor or central processing unit (CPU) in communication with the computer readable storage device 34 .
  • the computer readable storage device 34 is provided with data 36 , e.g. in the form of one or more databases, including a traffic control device database having a list of known traffic control devices and associated intersection positions.
  • a plurality of additional wireless communication devices 28 ′′ are also in communication with the server 32 .
  • the additional wireless communication devices 28 ′′ are configured to receive information from the server 32 , e.g. by accessing the databases 36 or by having information “pushed” from the server 32 to the additional wireless communication devices 28 ′′.
  • the plurality of additional wireless communication devices 28 ′′ are coupled to a plurality of additional vehicles.
  • the ADS 24 is capable of processing inputs from the sensors 26 to identify proximate traffic control devices.
  • Traffic control devices include, but are not limited to, stop signs, stop lights, yield signs, directional signs, and regulatory signs.
  • the ADS 24 may, for example, process an image obtained from an optical camera to identify upcoming traffic lights.
  • intersections e.g. intersections having a large number of lanes and/or intersections positioned at curves in roads
  • a known ADS may observe a traffic control device, e.g. a change in color of a traffic light, but have difficulty determining whether to obey the traffic control device based on the current lane of the vehicle absent additional information.
  • a current method of providing an ADS with additional information regarding an intersection includes human annotation of the intersection.
  • a human observer will note locations of traffic control devices in an intersection along with any applicable light timing.
  • the location and timing information is stored in a database, and subsequently distributed to an ADS. This process typically takes 5-10 minutes per intersection. As a result, annotating a large number of intersections may be time-consuming and expensive.
  • the method begins at block 40 .
  • a host vehicle is provided with sensors for detecting traffic control devices and for detecting motion of the host vehicle and target vehicles in the proximity of the host vehicle, as illustrated at block 42 . Examples of such sensors are discussed above with respect to FIG. 1 .
  • the host vehicle is an autonomous vehicle configured generally similarly to that illustrated in FIG. 1 .
  • the host vehicle may be, for example, a human-controlled vehicle, i.e. not under ADS control, provided with such sensors.
  • a traffic control device is detected by the sensors and identified, as illustrated at block 44 .
  • motion of target vehicles proximate the host vehicle and/or motion of the host vehicle is detected, as also illustrated at block 44 .
  • the detection and identification may be performed in various ways as appreciated by one of ordinary skill in the art.
  • a lane of traffic associated with the moving vehicle is identified, as illustrated at block 46 .
  • the lane of traffic may be associated with a current lane of the host vehicle, while if motion of a target vehicle was detected in block 44 , then the lane of traffic may be associated with the target vehicle.
  • the identification may be performed onboard the host vehicle, e.g. by the ADS 24 , or remotely, e.g. by the server 32 . An exemplary embodiment of lane identification will be discussed below with respect to FIG. 4 .
  • the lane of traffic identified in block 46 is then associated with the traffic control device, as illustrated in block 48 .
  • the association may be performed onboard the host vehicle, e.g. by the ADS 24 , or remotely, e.g. by the server 32 . Exemplary embodiments of this association will be discussed below with respect to FIGS. 5-7 .
  • the association of traffic lane with traffic control device is then stored in non-transient data storage, as illustrated in block 50 .
  • the data storage may be local to the vehicle, e.g. associated with the controller 22 , or remote, e.g. the storage device 34 associated with the server 32 .
  • a vehicle 10 according to the present disclosure having an ADS 24 may associate traffic lanes with traffic control devices in response to detected vehicle motion.
  • the ADS 24 may subsequently access the stored association to determine whether to obey an observed traffic control device based on the current lane of the vehicle.
  • the association may be communicated to the server 32 and stored in the storage device 34 for subsequent access by other vehicles having other ADS systems.
  • vehicles not under ADS control may also associate traffic lanes with traffic control devices and communicate the association to the server 32 .
  • traffic control devices for intersections may be mapped automatically without requiring human annotation.
  • FIG. 4 a second embodiment of a method for controlling a vehicle according to the present disclosure is illustrated in flowchart form. The method begins at block 60 .
  • a controlled intersection refers to an intersection of two roads having at least one traffic control device for providing instructions to vehicles on the roads.
  • control returns to operation 62 .
  • the algorithm does not proceed unless and until the host vehicle approaches a controlled intersection.
  • left is used to indicate a driver's side of the vehicle in a so-called right-hand-drive jurisdiction.
  • the directions may be other than those specifically discussed in these embodiments.
  • a solid yellow lane divider line e.g. as is generally used to divide opposing lanes of traffic or to indicate a road boundary
  • the current lane for the host vehicle may be identified as a left lane, as illustrated at block 68 .
  • the current lane may be identified as a left-most lane for a current road segment. Control then returns to operation 62 .
  • Channelized lanes refer to parallel lanes of traffic flow or lanes that are not opening, merging, or ending. Channelized lanes may in some cases be separated from the rest of the intersection by painted lines or raised barriers.
  • the current lane may be identified as “other”, e.g. neither a left-most lane nor a right-most lane, as illustrated at block 71 .
  • Such lanes may be referred to as center lanes.
  • a current lane may be estimated based on, e.g., a relative distance between a curb detected to the left of the vehicle and a curb detected to the right of the vehicle. Control then returns to operation 62
  • control proceeds to block 71 and the lane is identified as “other” as discussed above. Control then returns to operation 62 .
  • the current lane for the host vehicle may be identified as a right lane, as illustrated at block 76 .
  • the current lane may be identified as a right-most lane for a current road segment. Control then returns to operation 62 .
  • Road-edge-indicator features include, but are not limited to a detected road edge, a bike lane, or a line of parallel-parked cars.
  • control proceeds to block 76 and the current lane for the host vehicle may be identified as a right lane. Control then returns to operation 62 .
  • control proceeds to block 71 and the lane is identified as “other” as discussed above. Control then returns to operation 62 .
  • the determinations of operations 64 , 66 , 70 , 72 , 74 , and 78 may be made automatically, e.g. by the ADS 24 , according to various algorithms based on inputs received from the sensors 26 .
  • embodiments according to the present disclosure provide a method for identifying whether the host vehicle is in a left lane, right lane, or an other lane.
  • additional road-edge-indicator features may also be evaluated to confirm the presence of the vehicle in a given lane.
  • the identified lane for the host vehicle may be subsequently used for associating detected traffic flows with particular lanes.
  • FIG. 5 a third embodiment of a method for controlling a vehicle according to the present disclosure is illustrated in flowchart form. The method begins at block 80 .
  • a traffic control device e.g. one identified as a red turn arrow or red light
  • control returns to operation 82 .
  • the algorithm does not proceed unless and until the host vehicle stops at a traffic control device.
  • operation 82 determines whether the host vehicle has begun moving through the intersection with a green arrow or green traffic light identified, as illustrated at operation 84 .
  • the lane information may be obtained, for example, using the algorithm illustrated in FIG. 4 .
  • the road segment may be obtained from, for example, navigation system data.
  • the relationship of traffic control device position, traffic control device type and state, e.g. sign, turn arrow, or solid light, and current lane or trajectory are stored in a database, as illustrated at block 88 .
  • the data storage may be local to the vehicle, e.g. associated with the controller 22 , or remote, e.g. the storage device 34 associated with the server 32 . Control then returns to operation 82 .
  • insufficient information is available to associate the traffic control device to a current lane, as illustrated at block 90 .
  • potential trajectories for the current lane may still be tracked, e.g. a probabilistic determination may be used to predict a traffic control device likely associated to the current lane.
  • Observation of a stop or yield refers to either automated identification of a stop or yield traffic control device, to the host vehicle performing stop or yield behavior, or both. Stop or yield behavior may include, for example, a brief stop and subsequent resumption of vehicle motion.
  • control proceeds to operation 86 and the association of traffic control device and lane may be stored if adequate information is known, as discussed above.
  • operation 92 determines whether the host vehicle has traversed the intersection. If the determination of operation 92 is negative, a determination is made of whether the host vehicle has traversed the intersection, as illustrated at operation 94 .
  • control returns to operation 84 .
  • FIG. 6 a fourth embodiment of a method for controlling a vehicle according to the present disclosure is illustrated in flowchart form. The method begins at block 100 .
  • a traffic control device transition e.g. the host vehicle detecting a traffic light changing from red to green
  • Target vehicle motion with respect to the traffic control device transition refers to, for example, a detected target vehicle proximate the host vehicle transitioning from a stopped state to a moving state subsequent the traffic control device transition, indicating that the target vehicle motion is in response to the traffic control device transition.
  • proximate lanes may refer to lanes to the left or to the right of the current host vehicle lane or in cross-traffic with respect to the current host vehicle lane.
  • the relationship of traffic control device position, traffic control device type and state, e.g. sign, turn arrow, or solid light, and current lane or trajectory are stored in a database, as illustrated at block 108 .
  • the data storage may be local to the vehicle, e.g. associated with the controller 22 , or remote, e.g. the storage device 34 associated with the server 32 . Control then returns to operation 102 .
  • FIG. 7 a fifth embodiment of a method for controlling a vehicle according to the present disclosure is illustrated in flowchart form. The method begins at block 110 .
  • operation 112 determines whether the host vehicle detects a yellow light, yield light, or stop sign, as illustrated at operation 114 .
  • the relationship of traffic control device position, traffic control device type and state, e.g. sign, turn arrow, or solid light, and current lane or trajectory are stored in a database, as illustrated at block 116 .
  • the data storage may be local to the vehicle, e.g. associated with the controller 22 , or remote, e.g. the storage device 34 associated with the server 32 . Control then returns to operation 112 .
  • embodiments according to the present disclosure provide a method for identifying and associating traffic control devices based on behavior of both the host vehicle and of detected target vehicles in the vicinity of the host vehicle. This may enable faster and less expensive mapping of intersections for navigability by autonomous vehicles. Moreover, the mapping may be performed based on information from both autonomous vehicles and vehicles not under ADS control.

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US15/173,914 2016-06-06 2016-06-06 Sensor-based association of traffic control devices to traffic lanes for autonomous vehicle navigation Active US9891628B2 (en)

Priority Applications (3)

Application Number Priority Date Filing Date Title
US15/173,914 US9891628B2 (en) 2016-06-06 2016-06-06 Sensor-based association of traffic control devices to traffic lanes for autonomous vehicle navigation
CN201710341871.3A CN107463170A (zh) 2016-06-06 2017-05-16 用于自主车辆导航的交通控制设备与通车车道基于传感器的关联性
DE102017112306.7A DE102017112306A1 (de) 2016-06-06 2017-06-05 Sensorgestützte verbindung von verkehrssteuerungsvorrichtungen zur navigation von autonomen fahrzeugen im strassenverkehr

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US15/173,914 US9891628B2 (en) 2016-06-06 2016-06-06 Sensor-based association of traffic control devices to traffic lanes for autonomous vehicle navigation

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US20170351266A1 US20170351266A1 (en) 2017-12-07
US9891628B2 true US9891628B2 (en) 2018-02-13

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